query_data.py

from langchain.prompts.prompt import PromptTemplate
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI

# from langchain.llms import OpenAI

# from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT,QA_PROMPT

# _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a
# standalone question.
#
#
# Chat History:
# {chat_history}
# Follow Up Input: {question}
# Standalone question:"""
# CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
#
# template = """You are given an context and a question find an answer from the given
# context and provide an answer. If the answer is not in the context then
# simply say "No information found". Don't try to make an answer. And also don't use "According to the information
# provided in the given links," Question: {question} ========= {context} ========= Answer in Markdown: """
# QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])

_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a 
standalone question. 

Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)

prompt_template = """Give an answer to the question based on the context below and try to explain in detail and use 
bullet points if answer is long. 

{context}

Question: {question}
Helpful Answer:"""
QA_PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)


def get_chain(vectorstore):
    from langchain.chains.llm import LLMChain
    # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
    from langchain.chains.question_answering import load_qa_chain
    llm = ChatOpenAI(temperature=0)
    streaming_llm = ChatOpenAI(streaming=False, temperature=0, max_tokens=300, top_p=1)
    question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
    doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)

    # qa_chain = ConversationalRetrievalChain.from_llm(
    #     llm,
    #     vectorstore.as_retriever(search_kwargs={"k": 8, "include_metadata": True}),
    #     condense_question_prompt=CONDENSE_QUESTION_PROMPT,
    #     qa_prompt=QA_PROMPT
    # )

    qa_chain = ConversationalRetrievalChain(return_source_documents=True,
                                            retriever=vectorstore.as_retriever(
                                                search_kwargs={"k": 14, "include_metadata": True}),
                                            combine_docs_chain=doc_chain, question_generator=question_generator)
    return qa_chain